Skip to main content Skip to navigation
Department of Chemistry Aurora Clark

Clark, Aurora

Professor

Director Center for Institutional Research Computing

Deputy Director, IDREAM Energy Frontier Research Center

Laboratory Fellow, Pacific Northwest National Laboratory

Fellow, American Chemical Society

Fulmer 275
(509) 335-3362
auclark@wsu.edu
Twitter: @chemnetworks

 

Modeling and Simulation of Complex, Multicomponent Solutions and Their Interfaces

Our aim is to understand the impact of hierarchical organization and multiscale dynamic features upon chemical processes. As part of the DOE Energy Frontier Research Center on Interfacial Dynamics in Radioactive Environments and Materials (IDREAM) we use classical and ab-initio molecular dynamics simulations to examine highly concentrated electrolytes and their physicochemical characteristics from the molecular scale to solution rheology. Ultimately, this work helps to understand chemically extreme solution phase phenomena and provides the basic science needed to solve a variety of important industrial problems. This includes supporting the development of new treatment strategies for the remediation of nuclear waste stored at the Hanford site in Hanford Washington. See the recent Department of Energy highlight on the IDREAM EFRC: https://science.energy.gov/news/featured-articles/2019/03-08-19-2/

Our interest in complex solutions also permeates our chemical separations program, where the application is based upon developing a fundamental understanding that can support recycling of  high-value chemicals and elements, the production of high purity materials, and the remediation of polluted sites of interest to public health. Its societal impact cannot be understated and it is integrated throughout much of the industrial sector. Within our DOE Basic Energy Sciences Separations Program funded work, we focus upon the modeling and simulation of systems relevant to solvent extraction. There we seek to predict  the speciation of metal ions, their solvation and metal-ligand complexation reactions in multicomponent solutions,  and the properties of liquid:liquid interfaces and mechanisms of solute transport. These studies are helping to connect empirical observation of solvent extraction systems to a molecular and multiscale understanding of separation efficacy.

We collaborate with scientists at Argonne, Los Alamos, Oak Ridge, and Pacific Northwest National Laboratories to ensure strong integration of simulations and experimental observation. This includes the simulation of experimental data from national user facilities, like the Neutron Spallation Source at Oak Ridge National Laboratory. We strive to impart chemical realism to our simulations such that new chemical theories can be developed for the prediction of chemical processes in complex solutions and their interfaces.

Topological Data Analysis in Chemistry (TDA)

TDA is a branch of data science that focuses upon the geometry and shape (topology) of data. We use TDA to extract new information from simulation data and develop new TDA methods for chemical applications. In the context of chemistry we are extending TDA to understand networks of intermolecular interactions, and to understand complex hierarchical organizational patterns in soft matter. We use TDA to perform detailed sub-ensemble analysis of all observed environments in a simulation, and to determine the best correlation functions for how specific interaction are related to chemical outcomes. Our moleculaRnetworks and ChemNetworks software programs convert cartesian coordinates of chemical systems into intermolecular graphs that are then data-mined to understand new correlating relationships, look for patterns in interactions that are related to reactivity.
In a new NSF funded Harnessing the Data Revolution, Institute for Data-Intensive Research Frameworks grant, called Descriptors of Energy Landscapes Using Topological Data Analysis, we are leading a team of mathematicians, data scientists and chemists, to develop TDA methods that directly analyze the energy landscapes of chemical transformations and to combine TDA with machine learning to predict changes to their topological features under different conditions. For example, combinations of descriptors based upon Morse theory and persistent homology may be able to uniquely describe all of the features of an energy landscape for a homogeneous catalytic process such that we can predict how the product distribution ratios or turnover rates change as a function of solution conditions.